Access our SPSP poster here.
Give us your feedback here.
How do you define mass shootings?
Our data source, The
Violence Project, defines a public mass shooting as follows:
“a multiple homicide incident in which four or more
victims are murdered with firearms—not including the
offender(s)—within one event, and at least some of the murders occurred
in a public location or locations in close geographical proximity (e.g.,
a workplace, school, restaurant, or other public settings), and the
murders are not attributable to any other underlying criminal activity
or commonplace circumstance (armed robbery, criminal competition,
insurance fraud, argument, or romantic triangle).”
Which mass shootings did you include?
We included all 60 mass shootings in the United States from April
2013 to March 2023 (The
Violence Project). You can learn more about them in the table below.
Click on a column title to sort by it.
use_subreddit = name of the subreddit used for the analysis.
n_posts = number of Reddit comments containing at least 15 words.
n_authors = number of unique authors. min_date = date of earliest Reddit
post used (set to 6 weeks before the event). max_date = date of latest
Reddit post used (set to 6 weeks after the event).
Below is a map of the mass shootings included in our analysis. Each
circle is a mass shooting, and its radius is proportional to the number
of people killed and the number of people injured.
What are federally declared natural disasters?
When a disaster hits a state, the governor of said state can request
for a declaration by the President. The declaration paves way for the
state to receive federal assistance in recovering from the disaster. For
more information on how disasters get declared, check out this video
from Federal
Emergency Management Agency.
Which disasters did you include?
We included all 196 federally declared disasters that occurred in the
same counties as the the subreddit cities that suffered mass shootings
from April 2013 to March 2023. (FEMA).
In cases where a disaster was declared both as an emergency and a
major disaster (these are the two declaration types), we only kept one
observation.
Since each disaster declaration is filed by a state, you may see the
same disaster multiples times if multiple states filed requests. You may
also see that the same state filed multiple requests for the same
disaster—this is because each affected county within the state gets its
own request.
The disasters vary in duration. In all analyses, we used the day that
the disaster began, as recorded in the FEMA dataset.
You can learn more about the disasters included in the table below.
Click on a column title to sort by it.
use_subreddit = name of the subreddit used for the analysis.
n_posts = number of Reddit comments containing at least 15 words.
n_authors = number of unique authors. min_date = date of earliest Reddit
post used (set to 6 weeks before the event). max_date = date of latest
Reddit post used (set to 6 weeks after the event).
This map might help you situate the disasters. Each circle is a
disaster, and its radius is proportional to the dollar amount of federal
grant given to assist in recovery from the disaster.
What is Linguistic Inquiry and Word Count (LIWC)? Are there other
ways to detect language trends?
LIWC is a bag-of-words approach to studying language. In a nutshell,
it counts words that belong to a dictionary in a given piece of text.
These dictionaries are calibrated to correspond to elusive things like
cognitive processing and emotions. You can read more about it on the LIWC website. But as you know, these
elusive things are, well, elusive, and the dictionaries aren’t always
the best bet.
In our case, though, we have opted for LIWC because of its
simplicity, which makes interpretation of the results somewhat more
intuitive. Each measure (except for Analytic, which is a composite
score) is literally the % of words in the given text that are in the
dictionary.
There certainly are many other ways to look at language. If you think
there’s an approach that suits our use case, please
So what words are in the dictionaries you used?
Here are some examples:
- We-words: we, our, us, lets
- Prosocial words: care, help, thank, please
- I-words: I, me, my, myself
- They-words: they, their, them
Check out the LIWC user
manual for more details, including the frequencies of these words in
corpora like Twitter and the NYT.
Not all upheavals are created equal… have you thought about how
variations in the severity of the upheavals may affect your
results?
We think this is a super important point! In fact, we tried to redo
our analysis using some proxies for severity. The results in general
look like what you would predict: more severe upheavals saw larger
effects. But there are exceptions. Check them out for yourself in the
plots below.
Which severity proxies did you use?
For mass shootings, we used the number of people killed + the
number of people injured. For disasters, we used the total
amount of federal grant given to an affected region.
These are admittedly very crude proxies. For example, the number of
people affected by the disasters would be a better way to make
the proxy for disasters comparable to that for the mass shootings. The
proxies we used also don’t really get at how severe people
think these upheavals are. We think perceived severity
probably affects language use more than actual severity. It may be
better captured by something like the amount of media coverage/some
measure of how much people talked about the upheavals on-/off-line.
Can I look explore the language trends for each upheaval
separately?
We’re working on a more user-friendly way to present this. Coming
soon!
I have a better proxy/way of running this analysis in mind!
We would be so appreciative if you could share it in this feedback form!
What about time? You cover a 10-year span in your analysis. Maybe
something has changed over time in how people react to these
upheavals…
We think you’re totally onto something! Just anecdotally, we have
found seeing distressing news every day making us less able/willing to
grasp just how distressing these events actually are. Overtime we seem
to be slowly but surely being desensitized to things that should have
drawn out more reactions from us.
We broke down the analysis into three time bins in the plots below.
This is obviously super crude, and we’d love to hear that better idea
forming in your head right now: please share it in this form!
Did you run any statistical tests?
We ran a few paired t-tests (erring on the side of simplicity) to .
Here’s how we did it:
- We took the mean of linguistic measures from 6 weeks to 2 weeks
before the upheaval as baseline. Each author gets their own
baseline.
- For every week-long period from 1 week before the upheaval to 6
weeks after the upheaval, we compared the author’s linguistic measures
in that period to those in their baseline.
- If there lacks a statistically significant (we set the bar at the 3%
level) and substantially meaningful (well, this is up to your
interpretation—we’re again reminding you that all measures, except for
Analytic, are actual % of words in the text), then we say that the
person has reverted back to their baseline.
- Note: week 0 is the day of the upheaval.
There’re certainly better ways to do it! Please do share your
thoughts in this form.
Shootings
|
Characteristic
|
-1 wk, N = 26,962
|
baseline, N = 26,962
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.54 (1.37)
|
0.52 (1.09)
|
0.02
|
0.00, 0.04
|
0.031
|
|
prosocial
|
0.47 (1.18)
|
0.46 (0.96)
|
0.01
|
-0.01, 0.03
|
0.2
|
|
i
|
3.57 (3.48)
|
3.62 (3.01)
|
-0.05
|
-0.10, 0.00
|
0.042
|
|
they
|
1.37 (2.09)
|
1.40 (1.75)
|
-0.02
|
-0.05, 0.01
|
0.2
|
|
Characteristic
|
0 wk, N = 25,671
|
baseline, N = 25,671
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.56 (1.38)
|
0.52 (1.12)
|
0.04
|
0.02, 0.06
|
<0.001
|
|
prosocial
|
0.45 (1.15)
|
0.45 (0.98)
|
0.00
|
-0.02, 0.02
|
0.9
|
|
i
|
3.54 (3.49)
|
3.58 (2.97)
|
-0.05
|
-0.10, 0.00
|
0.068
|
|
they
|
1.39 (2.11)
|
1.40 (1.73)
|
-0.02
|
-0.05, 0.02
|
0.3
|
|
Characteristic
|
1 wk, N = 25,894
|
baseline, N = 25,894
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.38)
|
0.53 (1.11)
|
0.05
|
0.02, 0.07
|
<0.001
|
|
prosocial
|
0.49 (1.19)
|
0.46 (0.98)
|
0.04
|
0.02, 0.05
|
<0.001
|
|
i
|
3.51 (3.44)
|
3.61 (3.04)
|
-0.10
|
-0.15, -0.05
|
<0.001
|
|
they
|
1.37 (2.03)
|
1.39 (1.76)
|
-0.02
|
-0.05, 0.01
|
0.2
|
|
Characteristic
|
2 wk, N = 23,933
|
baseline, N = 23,933
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.52 (1.32)
|
0.52 (1.09)
|
0.00
|
-0.02, 0.02
|
0.7
|
|
prosocial
|
0.46 (1.19)
|
0.46 (1.00)
|
0.00
|
-0.02, 0.02
|
0.9
|
|
i
|
3.61 (3.49)
|
3.61 (3.01)
|
0.01
|
-0.05, 0.06
|
0.8
|
|
they
|
1.44 (2.14)
|
1.38 (1.72)
|
0.06
|
0.03, 0.09
|
<0.001
|
|
Characteristic
|
3 wk, N = 23,013
|
baseline, N = 23,013
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.54 (1.36)
|
0.52 (1.10)
|
0.01
|
-0.01, 0.03
|
0.3
|
|
prosocial
|
0.46 (1.16)
|
0.45 (0.96)
|
0.01
|
-0.01, 0.03
|
0.2
|
|
i
|
3.57 (3.47)
|
3.60 (2.99)
|
-0.03
|
-0.08, 0.02
|
0.3
|
|
they
|
1.44 (2.15)
|
1.39 (1.76)
|
0.05
|
0.02, 0.09
|
0.002
|
|
Characteristic
|
4 wk, N = 22,231
|
baseline, N = 22,231
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.51 (1.29)
|
0.52 (1.09)
|
-0.01
|
-0.03, 0.01
|
0.4
|
|
prosocial
|
0.46 (1.15)
|
0.46 (1.05)
|
0.00
|
-0.02, 0.02
|
0.9
|
|
i
|
3.52 (3.45)
|
3.62 (3.02)
|
-0.10
|
-0.15, -0.04
|
<0.001
|
|
they
|
1.43 (2.12)
|
1.39 (1.73)
|
0.04
|
0.01, 0.08
|
0.025
|
|
Characteristic
|
5 wk, N = 21,992
|
baseline, N = 21,992
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.38)
|
0.50 (1.07)
|
0.04
|
0.02, 0.06
|
<0.001
|
|
prosocial
|
0.45 (1.13)
|
0.44 (0.96)
|
0.00
|
-0.02, 0.02
|
0.8
|
|
i
|
3.57 (3.53)
|
3.62 (3.00)
|
-0.04
|
-0.10, 0.01
|
0.2
|
|
they
|
1.39 (2.12)
|
1.41 (1.76)
|
-0.02
|
-0.05, 0.02
|
0.4
|
|
Characteristic
|
6 wk, N = 19,901
|
baseline, N = 19,901
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.53 (1.37)
|
0.51 (1.05)
|
0.03
|
0.00, 0.05
|
0.027
|
|
prosocial
|
0.44 (1.15)
|
0.45 (0.98)
|
-0.01
|
-0.03, 0.01
|
0.3
|
|
i
|
3.6 (3.5)
|
3.6 (3.0)
|
0.00
|
-0.06, 0.06
|
0.9
|
|
they
|
1.40 (2.09)
|
1.38 (1.72)
|
0.02
|
-0.02, 0.06
|
0.3
|
Shootings - Zooming in on Week 1
|
Characteristic
|
Day 0, N = 7,012
|
baseline, N = 7,012
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.61 (1.51)
|
0.52 (1.03)
|
0.08
|
0.04, 0.12
|
<0.001
|
|
prosocial
|
0.53 (1.35)
|
0.44 (0.83)
|
0.09
|
0.06, 0.13
|
<0.001
|
|
i
|
3.20 (3.50)
|
3.46 (2.75)
|
-0.27
|
-0.36, -0.17
|
<0.001
|
|
they
|
1.46 (2.31)
|
1.41 (1.59)
|
0.05
|
-0.02, 0.11
|
0.14
|
|
Characteristic
|
Day 1, N = 7,309
|
baseline, N = 7,309
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.64 (1.50)
|
0.52 (1.04)
|
0.12
|
0.08, 0.16
|
<0.001
|
|
prosocial
|
0.52 (1.33)
|
0.47 (0.93)
|
0.05
|
0.02, 0.09
|
0.005
|
|
i
|
3.29 (3.55)
|
3.47 (2.81)
|
-0.18
|
-0.28, -0.09
|
<0.001
|
|
they
|
1.31 (2.08)
|
1.43 (1.65)
|
-0.12
|
-0.18, -0.06
|
<0.001
|
|
Characteristic
|
Day 2, N = 6,548
|
baseline, N = 6,548
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.59 (1.50)
|
0.54 (1.07)
|
0.04
|
0.00, 0.09
|
0.050
|
|
prosocial
|
0.54 (1.37)
|
0.46 (0.88)
|
0.08
|
0.04, 0.12
|
<0.001
|
|
i
|
3.39 (3.64)
|
3.52 (2.80)
|
-0.13
|
-0.23, -0.03
|
0.013
|
|
they
|
1.36 (2.19)
|
1.41 (1.61)
|
-0.05
|
-0.11, 0.02
|
0.14
|
|
Characteristic
|
Day 3, N = 6,381
|
baseline, N = 6,381
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.50)
|
0.53 (1.04)
|
0.04
|
0.00, 0.08
|
0.062
|
|
prosocial
|
0.50 (1.28)
|
0.46 (0.87)
|
0.04
|
0.00, 0.08
|
0.033
|
|
i
|
3.36 (3.60)
|
3.53 (2.86)
|
-0.17
|
-0.27, -0.07
|
0.001
|
|
they
|
1.39 (2.31)
|
1.40 (1.58)
|
-0.02
|
-0.08, 0.05
|
0.7
|
|
Characteristic
|
Day 4, N = 6,690
|
baseline, N = 6,690
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.49)
|
0.53 (0.98)
|
0.05
|
0.00, 0.09
|
0.029
|
|
prosocial
|
0.48 (1.30)
|
0.45 (0.89)
|
0.03
|
-0.01, 0.07
|
0.12
|
|
i
|
3.4 (3.7)
|
3.5 (2.8)
|
-0.08
|
-0.18, 0.02
|
0.13
|
|
they
|
1.45 (2.32)
|
1.38 (1.56)
|
0.06
|
0.00, 0.13
|
0.057
|
|
Characteristic
|
Day 5, N = 6,055
|
baseline, N = 6,055
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.56 (1.49)
|
0.53 (1.03)
|
0.03
|
-0.01, 0.07
|
0.2
|
|
prosocial
|
0.47 (1.27)
|
0.43 (0.84)
|
0.04
|
0.00, 0.07
|
0.070
|
|
i
|
3.47 (3.66)
|
3.49 (2.77)
|
-0.01
|
-0.12, 0.09
|
0.8
|
|
they
|
1.33 (2.26)
|
1.39 (1.56)
|
-0.05
|
-0.12, 0.02
|
0.13
|
|
Characteristic
|
Day 6, N = 5,696
|
baseline, N = 5,696
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.53 (1.38)
|
0.52 (1.00)
|
0.00
|
-0.04, 0.04
|
0.9
|
|
prosocial
|
0.44 (1.18)
|
0.46 (0.92)
|
-0.02
|
-0.06, 0.02
|
0.4
|
|
i
|
3.46 (3.66)
|
3.56 (2.75)
|
-0.10
|
-0.21, 0.01
|
0.071
|
|
they
|
1.35 (2.24)
|
1.38 (1.61)
|
-0.03
|
-0.10, 0.04
|
0.5
|
|
Characteristic
|
Day 7, N = 5,631
|
baseline, N = 5,631
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.50 (1.47)
|
0.50 (0.94)
|
0.00
|
-0.04, 0.04
|
0.9
|
|
prosocial
|
0.47 (1.27)
|
0.44 (0.90)
|
0.03
|
-0.01, 0.07
|
0.2
|
|
i
|
3.6 (3.8)
|
3.6 (2.8)
|
0.06
|
-0.06, 0.17
|
0.3
|
|
they
|
1.48 (2.31)
|
1.37 (1.58)
|
0.11
|
0.04, 0.18
|
0.002
|
Disasters
|
Characteristic
|
-1 wk, N = 81,061
|
baseline, N = 81,061
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.54 (1.32)
|
0.55 (1.12)
|
-0.01
|
-0.03, 0.00
|
0.018
|
|
prosocial
|
0.47 (1.16)
|
0.47 (0.98)
|
0.00
|
-0.01, 0.01
|
0.9
|
|
i
|
3.59 (3.52)
|
3.59 (2.98)
|
0.00
|
-0.03, 0.02
|
0.8
|
|
they
|
1.42 (2.15)
|
1.41 (1.74)
|
0.00
|
-0.02, 0.02
|
0.8
|
|
Characteristic
|
0 wk, N = 81,086
|
baseline, N = 81,086
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.37)
|
0.54 (1.12)
|
0.02
|
0.01, 0.03
|
<0.001
|
|
prosocial
|
0.46 (1.14)
|
0.46 (0.95)
|
0.00
|
-0.01, 0.01
|
0.3
|
|
i
|
3.6 (3.5)
|
3.6 (3.0)
|
0.02
|
-0.01, 0.05
|
0.14
|
|
they
|
1.40 (2.12)
|
1.40 (1.73)
|
-0.01
|
-0.03, 0.01
|
0.3
|
|
Characteristic
|
1 wk, N = 87,293
|
baseline, N = 87,293
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.64 (1.46)
|
0.55 (1.16)
|
0.09
|
0.08, 0.10
|
<0.001
|
|
prosocial
|
0.47 (1.17)
|
0.47 (1.00)
|
0.00
|
-0.01, 0.01
|
0.7
|
|
i
|
3.6 (3.5)
|
3.6 (3.1)
|
-0.02
|
-0.05, 0.01
|
0.2
|
|
they
|
1.34 (2.05)
|
1.41 (1.77)
|
-0.06
|
-0.08, -0.05
|
<0.001
|
|
Characteristic
|
2 wk, N = 78,905
|
baseline, N = 78,905
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.38)
|
0.55 (1.14)
|
0.02
|
0.01, 0.04
|
<0.001
|
|
prosocial
|
0.47 (1.17)
|
0.46 (0.96)
|
0.01
|
0.00, 0.02
|
0.063
|
|
i
|
3.57 (3.50)
|
3.59 (3.03)
|
-0.03
|
-0.06, 0.00
|
0.071
|
|
they
|
1.41 (2.10)
|
1.41 (1.77)
|
0.00
|
-0.02, 0.01
|
0.6
|
|
Characteristic
|
3 wk, N = 74,521
|
baseline, N = 74,521
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.36)
|
0.54 (1.12)
|
0.01
|
0.00, 0.03
|
0.028
|
|
prosocial
|
0.45 (1.15)
|
0.46 (0.97)
|
-0.01
|
-0.02, 0.00
|
0.2
|
|
i
|
3.6 (3.5)
|
3.6 (3.0)
|
0.00
|
-0.03, 0.03
|
0.9
|
|
they
|
1.42 (2.12)
|
1.41 (1.75)
|
0.01
|
-0.01, 0.03
|
0.5
|
|
Characteristic
|
4 wk, N = 72,887
|
baseline, N = 72,887
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.54 (1.32)
|
0.54 (1.12)
|
0.00
|
-0.02, 0.01
|
0.6
|
|
prosocial
|
0.45 (1.14)
|
0.45 (0.95)
|
0.00
|
-0.01, 0.01
|
0.9
|
|
i
|
3.57 (3.49)
|
3.58 (3.00)
|
-0.01
|
-0.04, 0.02
|
0.6
|
|
they
|
1.41 (2.12)
|
1.42 (1.75)
|
0.00
|
-0.02, 0.02
|
0.8
|
|
Characteristic
|
5 wk, N = 72,799
|
baseline, N = 72,799
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.36)
|
0.54 (1.12)
|
0.01
|
0.00, 0.03
|
0.021
|
|
prosocial
|
0.46 (1.16)
|
0.45 (0.95)
|
0.01
|
0.00, 0.02
|
0.078
|
|
i
|
3.54 (3.48)
|
3.56 (3.00)
|
-0.03
|
-0.06, 0.00
|
0.091
|
|
they
|
1.42 (2.12)
|
1.42 (1.74)
|
0.01
|
-0.01, 0.02
|
0.6
|
|
Characteristic
|
6 wk, N = 64,156
|
baseline, N = 64,156
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.36)
|
0.54 (1.11)
|
0.01
|
0.00, 0.02
|
0.2
|
|
prosocial
|
0.45 (1.13)
|
0.45 (0.94)
|
0.00
|
-0.01, 0.01
|
0.8
|
|
i
|
3.56 (3.51)
|
3.56 (2.98)
|
0.00
|
-0.03, 0.04
|
0.8
|
|
they
|
1.43 (2.14)
|
1.41 (1.71)
|
0.02
|
0.00, 0.04
|
0.035
|
Disasters - Zooming in on Week 1
|
Characteristic
|
Day 0, N = 21,835
|
baseline, N = 21,835
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.61 (1.50)
|
0.54 (1.02)
|
0.07
|
0.04, 0.09
|
<0.001
|
|
prosocial
|
0.46 (1.25)
|
0.47 (0.90)
|
0.00
|
-0.02, 0.02
|
0.9
|
|
i
|
3.6 (3.8)
|
3.6 (2.8)
|
0.04
|
-0.01, 0.10
|
0.12
|
|
they
|
1.32 (2.19)
|
1.38 (1.54)
|
-0.06
|
-0.09, -0.02
|
0.001
|
|
Characteristic
|
Day 1, N = 23,223
|
baseline, N = 23,223
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.63 (1.57)
|
0.55 (1.06)
|
0.08
|
0.05, 0.10
|
<0.001
|
|
prosocial
|
0.47 (1.25)
|
0.46 (0.90)
|
0.01
|
-0.01, 0.03
|
0.5
|
|
i
|
3.6 (3.8)
|
3.6 (2.9)
|
0.01
|
-0.05, 0.06
|
0.8
|
|
they
|
1.28 (2.18)
|
1.40 (1.61)
|
-0.12
|
-0.15, -0.09
|
<0.001
|
|
Characteristic
|
Day 2, N = 23,536
|
baseline, N = 23,536
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.66 (1.61)
|
0.55 (1.06)
|
0.11
|
0.08, 0.13
|
<0.001
|
|
prosocial
|
0.47 (1.27)
|
0.46 (0.91)
|
0.00
|
-0.02, 0.02
|
0.9
|
|
i
|
3.6 (3.8)
|
3.6 (2.9)
|
0.00
|
-0.06, 0.05
|
0.9
|
|
they
|
1.28 (2.17)
|
1.40 (1.60)
|
-0.11
|
-0.15, -0.08
|
<0.001
|
|
Characteristic
|
Day 3, N = 21,753
|
baseline, N = 21,753
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.66 (1.61)
|
0.56 (1.06)
|
0.10
|
0.08, 0.13
|
<0.001
|
|
prosocial
|
0.46 (1.26)
|
0.47 (0.91)
|
-0.01
|
-0.03, 0.01
|
0.4
|
|
i
|
3.6 (3.8)
|
3.6 (2.9)
|
-0.02
|
-0.08, 0.03
|
0.4
|
|
they
|
1.37 (2.27)
|
1.42 (1.63)
|
-0.05
|
-0.09, -0.01
|
0.007
|
|
Characteristic
|
Day 4, N = 22,842
|
baseline, N = 22,842
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.65 (1.56)
|
0.55 (1.05)
|
0.10
|
0.07, 0.12
|
<0.001
|
|
prosocial
|
0.48 (1.29)
|
0.47 (0.93)
|
0.01
|
-0.01, 0.03
|
0.4
|
|
i
|
3.6 (3.7)
|
3.6 (2.9)
|
-0.05
|
-0.11, 0.00
|
0.051
|
|
they
|
1.36 (2.25)
|
1.44 (1.65)
|
-0.08
|
-0.11, -0.04
|
<0.001
|
|
Characteristic
|
Day 5, N = 22,583
|
baseline, N = 22,583
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.69 (1.66)
|
0.54 (1.05)
|
0.14
|
0.12, 0.17
|
<0.001
|
|
prosocial
|
0.48 (1.28)
|
0.46 (0.91)
|
0.02
|
0.00, 0.04
|
0.027
|
|
i
|
3.6 (3.7)
|
3.6 (2.9)
|
-0.03
|
-0.08, 0.03
|
0.3
|
|
they
|
1.38 (2.25)
|
1.42 (1.63)
|
-0.04
|
-0.08, 0.00
|
0.030
|
|
Characteristic
|
Day 6, N = 22,149
|
baseline, N = 22,149
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.65 (1.60)
|
0.54 (1.02)
|
0.12
|
0.09, 0.14
|
<0.001
|
|
prosocial
|
0.48 (1.27)
|
0.47 (0.94)
|
0.01
|
-0.01, 0.03
|
0.5
|
|
i
|
3.6 (3.8)
|
3.6 (2.9)
|
0.02
|
-0.04, 0.08
|
0.5
|
|
they
|
1.36 (2.22)
|
1.40 (1.58)
|
-0.04
|
-0.08, -0.01
|
0.014
|
|
Characteristic
|
Day 7, N = 21,348
|
baseline, N = 21,348
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.61 (1.52)
|
0.55 (1.05)
|
0.06
|
0.04, 0.08
|
<0.001
|
|
prosocial
|
0.49 (1.29)
|
0.47 (0.89)
|
0.02
|
0.00, 0.04
|
0.029
|
|
i
|
3.6 (3.7)
|
3.6 (2.9)
|
-0.01
|
-0.06, 0.05
|
0.8
|
|
they
|
1.36 (2.23)
|
1.40 (1.58)
|
-0.04
|
-0.08, -0.01
|
0.020
|
Comparing shootings and disasters
Here we used two-sample t-tests. [1] “baseline:”
|
Characteristic
|
disaster, N = 403,599
|
shooting, N = 124,947
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.41)
|
0.54 (1.39)
|
0.02
|
0.01, 0.03
|
<0.001
|
|
prosocial
|
0.53 (1.29)
|
0.52 (1.29)
|
0.00
|
0.00, 0.01
|
0.4
|
|
i
|
3.8 (3.7)
|
3.9 (3.7)
|
-0.07
|
-0.09, -0.04
|
<0.001
|
|
they
|
1.36 (2.12)
|
1.35 (2.11)
|
0.01
|
0.00, 0.02
|
0.12
|
[1] “-1 wk:”
|
Characteristic
|
disaster, N = 144,388
|
shooting, N = 48,014
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.54 (1.42)
|
0.55 (1.47)
|
0.00
|
-0.02, 0.01
|
0.5
|
|
prosocial
|
0.51 (1.31)
|
0.51 (1.30)
|
0.00
|
-0.01, 0.02
|
0.5
|
|
i
|
3.8 (3.8)
|
3.7 (3.7)
|
0.02
|
-0.02, 0.05
|
0.4
|
|
they
|
1.37 (2.23)
|
1.33 (2.18)
|
0.03
|
0.01, 0.06
|
0.003
|
[1] “0 wk:”
|
Characteristic
|
disaster, N = 156,450
|
shooting, N = 49,799
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.57 (1.48)
|
0.56 (1.46)
|
0.01
|
0.00, 0.03
|
0.12
|
|
prosocial
|
0.51 (1.31)
|
0.51 (1.30)
|
0.00
|
-0.01, 0.02
|
0.7
|
|
i
|
3.8 (3.8)
|
3.7 (3.7)
|
0.07
|
0.04, 0.11
|
<0.001
|
|
they
|
1.35 (2.22)
|
1.34 (2.20)
|
0.01
|
-0.01, 0.03
|
0.4
|
[1] “1 wk:”
|
Characteristic
|
disaster, N = 202,835
|
shooting, N = 57,605
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.69 (1.64)
|
0.59 (1.49)
|
0.10
|
0.08, 0.11
|
<0.001
|
|
prosocial
|
0.55 (1.40)
|
0.58 (1.44)
|
-0.02
|
-0.04, -0.01
|
<0.001
|
|
i
|
3.8 (3.8)
|
3.6 (3.7)
|
0.18
|
0.14, 0.21
|
<0.001
|
|
they
|
1.28 (2.16)
|
1.34 (2.18)
|
-0.06
|
-0.08, -0.04
|
<0.001
|
[1] “2 wk:”
|
Characteristic
|
disaster, N = 164,886
|
shooting, N = 47,803
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.59 (1.52)
|
0.53 (1.42)
|
0.06
|
0.04, 0.07
|
<0.001
|
|
prosocial
|
0.54 (1.35)
|
0.51 (1.34)
|
0.02
|
0.01, 0.04
|
0.001
|
|
i
|
3.7 (3.8)
|
3.8 (3.8)
|
-0.07
|
-0.10, -0.03
|
<0.001
|
|
they
|
1.36 (2.21)
|
1.39 (2.22)
|
-0.03
|
-0.05, 0.00
|
0.027
|
[1] “3 wk:”
|
Characteristic
|
disaster, N = 152,529
|
shooting, N = 46,706
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.56 (1.48)
|
0.55 (1.47)
|
0.01
|
0.00, 0.03
|
0.13
|
|
prosocial
|
0.51 (1.31)
|
0.50 (1.29)
|
0.00
|
-0.01, 0.02
|
0.5
|
|
i
|
3.8 (3.8)
|
3.8 (3.8)
|
0.01
|
-0.03, 0.05
|
0.5
|
|
they
|
1.37 (2.22)
|
1.40 (2.23)
|
-0.03
|
-0.05, 0.00
|
0.024
|
[1] “4 wk:”
|
Characteristic
|
disaster, N = 150,859
|
shooting, N = 45,574
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.55 (1.45)
|
0.52 (1.40)
|
0.03
|
0.02, 0.05
|
<0.001
|
|
prosocial
|
0.51 (1.29)
|
0.51 (1.32)
|
-0.01
|
-0.02, 0.01
|
0.4
|
|
i
|
3.8 (3.8)
|
3.7 (3.7)
|
0.01
|
-0.03, 0.05
|
0.5
|
|
they
|
1.38 (2.23)
|
1.39 (2.23)
|
-0.01
|
-0.03, 0.01
|
0.4
|
[1] “5 wk:”
|
Characteristic
|
disaster, N = 151,400
|
shooting, N = 46,332
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.56 (1.47)
|
0.55 (1.46)
|
0.01
|
-0.01, 0.02
|
0.2
|
|
prosocial
|
0.51 (1.32)
|
0.51 (1.33)
|
0.00
|
-0.01, 0.02
|
0.6
|
|
i
|
3.7 (3.8)
|
3.8 (3.8)
|
-0.05
|
-0.09, -0.01
|
0.008
|
|
they
|
1.37 (2.22)
|
1.35 (2.22)
|
0.02
|
0.00, 0.05
|
0.044
|
[1] “6 wk:”
|
Characteristic
|
disaster, N = 133,375
|
shooting, N = 42,080
|
Difference
|
95% CI
|
p-value
|
|
we
|
0.56 (1.45)
|
0.54 (1.48)
|
0.01
|
0.00, 0.03
|
0.2
|
|
prosocial
|
0.51 (1.31)
|
0.50 (1.32)
|
0.01
|
-0.01, 0.02
|
0.3
|
|
i
|
3.7 (3.8)
|
3.8 (3.8)
|
-0.06
|
-0.10, -0.02
|
0.004
|
|
they
|
1.38 (2.24)
|
1.36 (2.20)
|
0.02
|
0.00, 0.04
|
0.11
|
Can I see the graphs on your poster again?
Absolutely!
Can I see trends in other linguistic markers?
Absolutely!